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pro vyhledávání: '"XU, GUOQING HARRY"'
Autor:
Thorpe, John, Zhao, Pengzhan, Eyolfson, Jonathan, Qiao, Yifan, Jia, Zhihao, Zhang, Minjia, Netravali, Ravi, Xu, Guoqing Harry
DNN models across many domains continue to grow in size, resulting in high resource requirements for effective training, and unpalatable (and often unaffordable) costs for organizations and research labs across scales. This paper aims to significantl
Externí odkaz:
http://arxiv.org/abs/2204.12013
Autor:
Wang, Chenxi, Qiao, Yifan, Ma, Haoran, Liu, Shi, Zhang, Yiying, Chen, Wenguang, Netravali, Ravi, Kim, Miryung, Xu, Guoqing Harry
Remote memory techniques for datacenter applications have recently gained a great deal of popularity. Existing remote memory techniques focus on the efficiency of a single application setting only. However, when multiple applications co-run on a remo
Externí odkaz:
http://arxiv.org/abs/2203.09615
Autor:
Padmanabhan, Arthi, Agarwal, Neil, Iyer, Anand, Ananthanarayanan, Ganesh, Shu, Yuanchao, Karianakis, Nikolaos, Xu, Guoqing Harry, Netravali, Ravi
Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth overheads and privacy violations, but in doing so, face an ever-growing resource tension. Most notably, edge-box GPUs lack the memory needed to concurrently house
Externí odkaz:
http://arxiv.org/abs/2201.07705
Event-driven architectures are broadly used for systems that must respond to events in the real world. Event-driven applications are prone to concurrency bugs that involve subtle errors in reasoning about the ordering of events. Unfortunately, there
Externí odkaz:
http://arxiv.org/abs/2111.05290
Autor:
Tang, Bowen, Wu, Chenggang, Wang, Zhe, Jia, Lichen, Yew, Pen-Chung, Cheng, Yueqiang, Zhang, Yinqian, Wang, Chenxi, Xu, Guoqing Harry
Speculative execution techniques have been a cornerstone of modern processors to improve instruction-level parallelism. However, recent studies showed that this kind of techniques could be exploited by attackers to leak secret data via transient exec
Externí odkaz:
http://arxiv.org/abs/2107.08367
Autor:
Thorpe, John, Qiao, Yifan, Eyolfson, Jonathan, Teng, Shen, Hu, Guanzhou, Jia, Zhihao, Wei, Jinliang, Vora, Keval, Netravali, Ravi, Kim, Miryung, Xu, Guoqing Harry
A graph neural network (GNN) enables deep learning on structured graph data. There are two major GNN training obstacles: 1) it relies on high-end servers with many GPUs which are expensive to purchase and maintain, and 2) limited memory on GPUs canno
Externí odkaz:
http://arxiv.org/abs/2105.11118
Byte-addressable persistent memory, such as Intel/Micron 3D XPoint, is an emerging technology that bridges the gap between volatile memory and persistent storage. Data in persistent memory survives crashes and restarts; however, it is challenging to
Externí odkaz:
http://arxiv.org/abs/2004.00262
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